This sponsored article is brought to you by Applied Materials.<\/em><\/p>\n At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs \u2014 such as those achieved under the Human Genome Project \u2014 required a new operating paradigm: Concentrate the world\u2019s best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and siloed innovation simply cannot keep pace.<\/p>\n Today\u2019s AI era is creating an engineering race with similar demands. Every company is pushing to deliver higher-performance AI systems, faster. But performance is no longer defined by compute alone. AI workloads are increasingly dominated by the movement of data: In many cases, moving bits consumes as much \u2014 or more \u2014 energy than compute itself. As a result, reducing energy per bit can extend system\u2011level performance alongside gains in peak compute.<\/p>\n The path to energy\u2011efficient AI therefore runs through system\u2011level engineering, spanning three tightly interconnected domains:<\/span><\/p>\n These domains can no longer be optimized independently. Gains in logic efficiency stall without sufficient memory bandwidth. Advances in memory bandwidth fall short if packaging cannot deliver proximity within thermal and mechanical constraints. Packaging, in turn, is constrained by the precision of both front\u2011end device fabrication and back\u2011end integration processes.<\/p>\n In the angstrom era, the hardest problems arise at the boundaries \u2014 between compute and memory in the package, front\u2011end and back\u2011end integration, and the tightly coupled process steps needed for precise 3D fabrication. And it is precisely this boundary\u2011driven complexity where the traditional innovation model breaks down.<\/p>\n For decades, the semiconductor industry\u2019s R&D model has resembled a relay race. Capabilities are developed in one part of the ecosystem, handed off downstream through integration and manufacturing, evaluated by chip and system designers, and only then fed back for the next iteration. That model worked when progress was dominated by relatively modular steps that could be scaled independently and simply dropped into the manufacturing flow.<\/p>\n But the AI timeline has upended these rules. At angstrom\u2011scale dimensions, the physics enforces inescapable coupling across the entire stack: materials choices shape integration schemes; integration defines design rules; design rules dictate power delivery; wiring sets thermal budgets; and thermals ultimately constrain packaging scaling. System architects simply cannot wait 10\u201315 years for each major semiconductor technology inflection to mature.<\/p>\n Representing a roughly $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history.<\/p>\n A long\u2011term perspective is essential to align materials innovation with emerging device architectures \u2014 and to develop the tools and processes required to integrate both with manufacturable precision. At Applied Materials, together with our customers, we are charting a course across the next 3\u20134 generations, extending as far as 10 years down the roadmap.<\/p>\n The angstrom era demands that we break down silos and bring together the industry\u2019s best minds \u2014 from leading companies to leading academic institutions. If the problem is coupled, the solution must be coupled. If the timeline is compressed, the learning loop must be compressed. It\u2019s not enough to just innovate \u2014 we must innovate how <\/em>we innovate.<\/p>\n This is the challenge that Applied Materials EPIC Center is designed to solve.<\/p>\n Representing a roughly US $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. When it opens in 2026, it will deliver state\u2011of\u2011the\u2011art cleanroom capabilities built from the ground up to shorten the path from early\u2011stage research to full\u2011scale manufacturing. But the facilities are only one component of the model. EPIC is also a platform, an operating system for high-velocity co\u2011innovation that revolutionizes how ideas move from the lab to the fab.<\/p>\n The EPIC model compresses the traditional workflow. Customer engineers work side\u2011by\u2011side with Applied technologists from day one \u2014 moving beyond isolated process optimization and downstream handoffs. Within a shared, secure environment, EPIC tightly integrates atomistic modeling, test vehicles, process development, validation, and metrology feedback. Constraints that once surfaced late in development are identified and addressed early.<\/span><\/p>\n The result is a potentially 2x faster path that benefits the entire ecosystem under one roof:<\/p>\n Building on decades of co\u2011development, we are reinventing the innovation pipeline with our partners across logic, memory, and advanced packaging to deliver the next leap in energy\u2011efficient AI.<\/p>\n Logic remains the engine of AI compute. In the angstrom era, however, system\u2011level gains are increasingly constrained by power and energy. Extending AI performance now depends on architectures that deliver more performance per watt \u2014 accelerating the move to 3D devices such as gate\u2011all\u2011around (GAA) transistors, which boost density within a compact footprint while preserving power efficiency.<\/p>\n These architectural shifts are unfolding at unprecedented scale, with the logic roadmap already extending beyond first\u2011generation GAA toward more advanced designs. One key example is GAA with backside power delivery, which relocates thick power lines to the backside of the wafer, reducing resistive losses and freeing front\u2011side routing for tighter logic cell integration. Another example brings adjacent GAA PMOS and NMOS transistors closer together while inserting a dielectric isolation wall between them to minimize electrical interference. Further out, complementary FETs (CFETs) push density scaling even more by stacking PMOS and NMOS devices directly atop one another.<\/span><\/p>\n While these architectures deliver compelling gains in performance per watt and logic density without relying solely on tighter lithography, they significantly raise integration complexity. Manufacturing a single GAA device today can involve more than 2,000 tightly interdependent process steps. At the same time, wiring stacks continue to grow taller and denser to connect these advanced logic devices. Modern leading\u2011edge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring.<\/p>\n At this level of complexity, the process steps used to create these precise 3D devices and wiring stacks cannot be optimized independently. Design and process must evolve in lockstep, and materials innovation and fabrication methods must advance alongside device architecture. EPIC\u2019s co\u2011innovation model is designed to accelerate exactly this convergence \u2014 enabling logic compute to continue advancing the frontiers of AI at the pace the roadmap demands.<\/span><\/p>\n At the same time, the AI computing era is fundamentally reshaping how data is generated, moved, and processed \u2014 making memory technologies, especially DRAM, central to delivering the energy\u2011efficient performance AI systems require. As models grow larger and more data\u2011hungry, the DRAM roadmap is shifting toward architectures that deliver higher density, greater bandwidth, and faster access per watt.<\/p>\n At the DRAM cell level, this shift is driving a transition from 6F\u00b2 buried\u2011channel array transistors (BCAT) to more compact 4F\u00b2 architectures, which orient the transistor vertically to boost density and reduce chip area. Looking beyond 4F\u00b2, sustaining gains in performance per watt will require moving past what 2D scaling alone can deliver. The industry is therefore turning to 3D DRAM, stacking memory cells vertically to add capacity within a constrained footprint. As these structures grow taller and aspect ratios intensify, high-mobility materials engineering in three dimensions becomes increasingly critical to performance and reliability.<\/p>\n Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring. One emerging approach places select periphery functions beneath the DRAM array by bonding two wafers \u2014 one optimized for the DRAM cells and the other for CMOS logic \u2014 using multiple wiring layers.<\/p>\n In parallel, DRAM performance is being extended by leveraging logic\u2011proven enhancers in the memory periphery. These include mobility boosters such as embedded silicon germanium and stress films, along with wiring upgrades like improved low\u2011k dielectrics and advanced copper interconnects. Memory manufacturers are also transitioning periphery transistors from planar devices to FinFET architectures, following the logic roadmap to further improve I\/O speed. These valuable inflections are central to EPIC\u2019s mission \u2014 where they can be co-developed and rapidly validated for next\u2011generation memory systems.<\/p>\n As data movement becomes the dominant energy cost in AI systems, advanced packaging has emerged as a critical lever for improving system\u2011level efficiency\u2014shortening interconnect distances, increasing bandwidth density, and reducing the power required to move data between logic and memory.<\/p>\n High\u2011bandwidth memory (HBM) marks a major inflection along this path. By stacking DRAM dies \u2014 scaling to 16 layers and beyond \u2014 and placing memory much closer to the processor, HBM enables rapid access to ever\u2011larger working datasets. This delivers step\u2011function gains in both bandwidth and energy efficiency.<\/p>\n More broadly, the rise of 3D packages such as HBM underscores why advanced packaging is becoming central to the AI era. Packaging now addresses system\u2011level constraints that logic and memory device scaling alone can no longer overcome. It also enables a move away from monolithic systems\u2011on\u2011chip toward chiplet\u2011based architectures, as AI workloads increasingly demand flexible designs that combine logic, memory, and specialized accelerators optimized for specific tasks.<\/p>\n A vital technology powering this roadmap is hybrid bonding. With interconnect pitches approaching those of on\u2011chip wiring, conventional bumps and microbumps run into fundamental limits in density, power, and signal integrity. Hybrid bonding removes these barriers by allowing dramatically higher interconnect and I\/O density, supporting a broad range of chiplet architectures \u2014 from memory stacking to tighter compute\u2011memory integration.<\/p>\n As bonded structures like HBM stacks grow larger and more complex, warpage control, die placement, stack alignment, and thermal management become first\u2011order challenges. EPIC tackles these and other high\u2011value advanced\u2011packaging challenges through early, parallel co\u2011innovation across materials, integration, and manufacturing.<\/p>\n Across logic, memory, and advanced packaging, our industry faces an ambitious roadmap that promises significant gains in energy efficiency for AI systems. But realizing that potential demands breakthrough materials innovation at a time when feature sizes are shrinking, interfaces are multiplying, and process interdependencies are escalating. These challenges cannot be solved on 10\u201315\u2011year timelines under the traditional relay\u2011race model. We must break down silos, align earlier across the ecosystem, and parallelize learning to keep pace with AI\u2019s demands.<\/p>\n In the AI era, progress will be defined by the speed at which lightbulb moments turn into manufacturing and commercialization reality. The only viable path forward is a new innovation model \u2014 and EPIC is how we are driving it.<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":" This sponsored article is brought to you by Applied Materials. At pivotal moments in history, progress has required more than individual brilliance. 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The Traditional R&D Workflow Is Too Slow for Angstrom\u2011Era AI<\/h2>\n
EPIC: A Center and Platform for High\u2011Velocity Co\u2011Innovation<\/h2>\n
![\"Diagram](\"https:\/\/spectrum.ieee.org\/media-library\/diagram-comparing-traditional-and-epic-chip-innovation-timelines-showing-2x-faster-path.png?id=66661836&width=980\")
EPIC is a platform, an operating system for high-velocity co\u2011innovation that revolutionizes how ideas move from the lab to the fab.<\/small>Applied Materials<\/small><\/p>\n\n
Accelerating Advanced Logic<\/h2>\n
Powering the Memory Roadmap<\/h2>\n
Driving System Scaling With Advanced Packaging<\/h2>\n
Bringing It All Together<\/h2>\n